| //mypedia -- build a short, balanced reference page about a name, using ChatGPT. | |
| const name = args [0]; | |
| if (name === undefined) { | |
| console.log ("Usage: belt mypedia.belt \"name\""); | |
| } | |
| else { | |
| //first-cut prompt -- refine the wording here. | |
| const prompt = "Build a short, balanced reference page about " + name + ". " | |
| + "Follow the structure of a Wikipedia article but keep it brief -- an outline of the basic facts, not a long detailed treatment. " |
| name | code-refactor-review |
|---|---|
| description | Reviews code changes for reuse, composition, codebase consistency, and slop. Use when asked to review PRs/diffs, check code reuse, composition, cleanliness, or whether code fits the codebase. |
Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.
Author and Sole Rightsholder: AGI885
Priority Registration Date: June 2026
Status: All Rights Reserved
This repository formally establishes the intellectual property, copyright, and architectural priority of the user AGI885 (GitHub: AGI885) for the development of a True Artificial General Intelligence (AGI) system. The framework is built upon the principles of evolutionary adaptation and causal learning within isolated 2D and 3D simulation environments (including, but not limited to, the Minecraft platform). The core architecture completely bypasses the standard next-token prediction paradigm of modern LLMs, transitioning instead to genuine causal learning and establishing an emergent sense of agency.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
